Dimensional Measurement Uncertainty and Inspection Planning

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9622692 Kurfess Modern machining facilities can produce parts with complex and precise geometry, but high precision coordinate measuring machines (CMMs) are still needed to validate these geometries. The CMMs measure Cartesian coordinates of points on the part surface, and these points are fit to nominal part geometries using criteria like mini-max or least squares. These methods yield conflicting or inconsistent results because measurement uncertainty has not been addressed by the analysis. This research will develop methods that relate multivariate confidence intervals, the optimal location of point measurements for a feature, and the number of points in a way that maximizes information content. An experimental program, in cooperation with industry, will test these advances in processing CMM data. This research impacts both productivity and quality in manufacture. For example, one application of this methodology is to specify the number of points to be collected on the CMM based on time or cost constraints, and then generate a sampling plan and an estimate of the confidence regions for the measured features. Alternatively, if a specified degree of accuracy is needed, then the methodology can provide a sampling plan for making measurements and specify the number of points needed to achieve a the specified confidence region.